Staff AI Analytics Engineer - People Insights

Intuit, Inc.; New York (NY); Presencial; USD 160.000 - 216.000; Jornada completa

Descripción de la vacante

Intuit, Inc. is seeking a Staff Business Data Analyst on the PART team to drive applied AI experimentation across talent analytics and workforce planning. The role blends hands‑on AI prototyping with business insight, enabling prototypes to scale within the enterprise.

You will partner with enterprise AI Transformation and HR to align standards, build reusable analytics patterns, and guide prototypes from concept to production, ensuring governance and provenance across outputs.

Formación

• Bachelor's degree in AI, Data Analytics, Computer Science, Business, Human Resources, Industrial/Organizational Psychology, or a related field.
• 7+ years of experience in business/data analytics or applied AI/ML roles, with demonstrated hands‑on ownership of building and shipping solutions.
• Direct, hands‑on experience building and deploying AI agents or LLM‑powered tools/prototypes.
• Working knowledge of data warehouse, data pipeline, and data architecture concepts, and how they affect AI/analytics readiness at scale.
• Experience with dashboard/BI design and self‑serve or conversational analytics patterns, including how to certify data sources for broader consumption.
• Comfort defining confidence tiers for AI‑generated outputs, and reliably estimating and scoping ambiguous, large initiatives.
• Strong point of view on evaluating technical feasibility vs. speculative AI use cases, with the judgment to make build/no‑build recommendations.
• Excellent communication skills, with a track record of translating technical experimentation into decisions for senior stakeholders and executive audiences.
• Ability to operate autonomously in ambiguous, undefined problem spaces — this role sits ahead of established enterprise AI patterns.

Responsabilidades

• AI Agents and Proof‑of‑Concept Development & Experimentation.
• Design, build, and iterate rapid AI proof‑of‑concepts and experiments across Talent Analytics, workforce planning, talent acquisition, and related people‑data use cases, using modern AI/LLM tooling to validate ideas before committing engineering investment.
• Design agent‑assisted analytics for these pilots with explicit decision boundaries — specifying what an agent may decide or act on autonomously versus what must escalate to a human, grounded in governed data.
• Continuously scan for new AI capabilities, tools, and use cases applicable to talent analytics and workforce planning, maintaining a point of view on what's worth piloting next.
• Self‑Serve Analytics, Visualization & Output Trust.
• Define the dashboard and visualization patterns for prototypes that graduate toward self‑serve or conversational analytics, and identify which underlying data sources are certified for that use.
• Label AI‑generated outputs and prototypes by confidence tier (e.g., exploratory, validated, decision‑ready) so stakeholders know how much weight an insight can bear, and ensure provenance is traceable.
• Enterprise AI Partnership & Standards Alignment.
• Partner with the AI Transformation team to align local experimentation with enterprise AI standards, tooling, and roadmap, so pilots aren't reinvented or left orphaned.
• Bring enterprise capabilities into the team's use cases and feed learnings back to the enterprise team.
• Contextual & Semantic Layer Contribution.
• Work with Technology and HR organization to contribute to and consume the HR contextual layer, encoding the team's domain definitions and business logic so both people and agents can rely on them consistently as shared context infrastructure matures.
• Planning, Roadmap Integration & Production Handoff.
• Provide reliable estimates and establish a shared, spec‑writing discipline for ambiguous, large‑scope initiatives so that AI‑assisted work is planned with the same rigor as any other delivery.
• Embed AI into the team's analytics product roadmap, identifying where AI changes what a product can do rather than bolting it onto existing outputs, and sequencing prototypes so validated ideas have a defined path forward.
• Define what moves from prototype to production, working with data engineering and product to hand off validated solutions with documentation, authored evaluation criteria, and the context needed to sustain them, closing the loop by routing outcomes back into future prototyping.
• Technical Feasibility, Risk & Executive Communication.
• Evaluate what's technically feasible versus speculative, and translate that into clear, actionable recommendations for leadership.
• Communicate technical feasibility, risk, and recommended next steps for AI initiatives to senior stakeholders.
• Capability Building.
• Raise applied‑AI capability across the team and partner groups by establishing reusable patterns, tooling standards, and working practices so AI‑enabled delivery becomes the team's default rather than one person's specialty.
• Coach senior analysts and partner teams to run AI‑assisted projects and agents independently, building their judgment rather than just delivering outputs for them.

Conocimientos

Hands-on ownership

AI agents

Data architecture concepts

Dashboard design

Excellent communication

Autonomy in ambiguity

Feasibility assessment

Educación

Bachelor's degree in AI/Data Analytics/CS/Business/HR/IO psychology

Herramientas

Claude / GPT

Workday HCM data

Avature

Descripción del empleo

Intuit, Inc. is seeking a Staff Business Data Analyst on the PART team to drive applied AI experimentation across talent analytics and workforce planning. The role blends hands‑on AI prototyping with business insight, enabling prototypes to scale within the enterprise.

You will partner with enterprise AI Transformation and HR to align standards, build reusable analytics patterns, and guide prototypes from concept to production, ensuring governance and provenance across outputs.